{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from agots.multivariate_generators.multivariate_data_generator import MultivariateDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985941</td>\n",
       "      <td>-0.144821</td>\n",
       "      <td>-0.186724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.985941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.129324</td>\n",
       "      <td>-0.187107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>-0.144821</td>\n",
       "      <td>-0.129324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>-0.186724</td>\n",
       "      <td>-0.187107</td>\n",
       "      <td>0.197286</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x0        x1        x2        x3\n",
       "x0  1.000000  0.985941 -0.144821 -0.186724\n",
       "x1  0.985941  1.000000 -0.129324 -0.187107\n",
       "x2 -0.144821 -0.129324  1.000000  0.197286\n",
       "x3 -0.186724 -0.187107  0.197286  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1337)\n",
    "\n",
    "STREAM_LENGTH = 200\n",
    "N = 4\n",
    "K = 2\n",
    "\n",
    "dg = MultivariateDataGenerator(STREAM_LENGTH, N, K)\n",
    "df = dg.generate_baseline(initial_value_min=-4, initial_value_max=4)\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.060530</td>\n",
       "      <td>-2.887943</td>\n",
       "      <td>2.419412</td>\n",
       "      <td>0.908175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.773117</td>\n",
       "      <td>-3.003151</td>\n",
       "      <td>2.359755</td>\n",
       "      <td>0.756587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.816198</td>\n",
       "      <td>-2.953174</td>\n",
       "      <td>2.774531</td>\n",
       "      <td>0.618809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.350169</td>\n",
       "      <td>-2.929736</td>\n",
       "      <td>2.893389</td>\n",
       "      <td>1.019772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.085558</td>\n",
       "      <td>-3.566645</td>\n",
       "      <td>3.299516</td>\n",
       "      <td>0.859867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.177671</td>\n",
       "      <td>-3.340701</td>\n",
       "      <td>3.712991</td>\n",
       "      <td>0.423561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.012698</td>\n",
       "      <td>-3.302543</td>\n",
       "      <td>3.611960</td>\n",
       "      <td>0.424597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.783180</td>\n",
       "      <td>-3.384022</td>\n",
       "      <td>3.363775</td>\n",
       "      <td>0.011063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.590633</td>\n",
       "      <td>-3.465373</td>\n",
       "      <td>3.730589</td>\n",
       "      <td>-0.406819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.893002</td>\n",
       "      <td>-3.486088</td>\n",
       "      <td>3.773050</td>\n",
       "      <td>0.055843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.488150</td>\n",
       "      <td>-3.474417</td>\n",
       "      <td>4.014207</td>\n",
       "      <td>0.013911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.351290</td>\n",
       "      <td>-3.912176</td>\n",
       "      <td>3.804653</td>\n",
       "      <td>0.454455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.592446</td>\n",
       "      <td>-3.730252</td>\n",
       "      <td>3.551222</td>\n",
       "      <td>0.693068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.252394</td>\n",
       "      <td>-3.458582</td>\n",
       "      <td>3.352049</td>\n",
       "      <td>0.833815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.615042</td>\n",
       "      <td>-3.666618</td>\n",
       "      <td>2.875478</td>\n",
       "      <td>0.793399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.904937</td>\n",
       "      <td>-3.509938</td>\n",
       "      <td>3.097578</td>\n",
       "      <td>0.371861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.410280</td>\n",
       "      <td>-3.774180</td>\n",
       "      <td>3.353890</td>\n",
       "      <td>0.585673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.373855</td>\n",
       "      <td>-4.040941</td>\n",
       "      <td>3.002045</td>\n",
       "      <td>0.387076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.254923</td>\n",
       "      <td>-3.688319</td>\n",
       "      <td>3.142167</td>\n",
       "      <td>0.661533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.275424</td>\n",
       "      <td>-3.755768</td>\n",
       "      <td>4.109229</td>\n",
       "      <td>19.434913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.145376</td>\n",
       "      <td>-3.758103</td>\n",
       "      <td>3.602231</td>\n",
       "      <td>7.048872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.121012</td>\n",
       "      <td>-4.098771</td>\n",
       "      <td>3.485711</td>\n",
       "      <td>15.251952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.484584</td>\n",
       "      <td>-4.130221</td>\n",
       "      <td>3.791785</td>\n",
       "      <td>4.344360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.389605</td>\n",
       "      <td>-3.927507</td>\n",
       "      <td>3.437481</td>\n",
       "      <td>15.570344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.330106</td>\n",
       "      <td>-3.643611</td>\n",
       "      <td>4.395647</td>\n",
       "      <td>9.284515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.970960</td>\n",
       "      <td>-3.883088</td>\n",
       "      <td>4.682349</td>\n",
       "      <td>4.914358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.291141</td>\n",
       "      <td>-4.174954</td>\n",
       "      <td>5.074739</td>\n",
       "      <td>1.293672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.688795</td>\n",
       "      <td>-3.788701</td>\n",
       "      <td>5.482640</td>\n",
       "      <td>16.600688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.117202</td>\n",
       "      <td>-3.886541</td>\n",
       "      <td>5.419276</td>\n",
       "      <td>14.932876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>8.893828</td>\n",
       "      <td>-19.481609</td>\n",
       "      <td>0.611777</td>\n",
       "      <td>-1.053146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>8.664630</td>\n",
       "      <td>-18.842794</td>\n",
       "      <td>0.852345</td>\n",
       "      <td>-0.571571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>9.126901</td>\n",
       "      <td>-19.306399</td>\n",
       "      <td>0.883148</td>\n",
       "      <td>-0.075540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>9.070452</td>\n",
       "      <td>-19.085070</td>\n",
       "      <td>1.294560</td>\n",
       "      <td>0.027764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>8.710409</td>\n",
       "      <td>-19.157064</td>\n",
       "      <td>1.650591</td>\n",
       "      <td>-0.286059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>8.721622</td>\n",
       "      <td>-19.446004</td>\n",
       "      <td>1.915065</td>\n",
       "      <td>-0.252924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>8.927362</td>\n",
       "      <td>-19.549335</td>\n",
       "      <td>2.106074</td>\n",
       "      <td>0.236873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>8.746750</td>\n",
       "      <td>-19.398734</td>\n",
       "      <td>2.012135</td>\n",
       "      <td>0.095387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>8.517075</td>\n",
       "      <td>-19.919382</td>\n",
       "      <td>1.749971</td>\n",
       "      <td>-0.229970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>8.925363</td>\n",
       "      <td>-19.494523</td>\n",
       "      <td>1.598497</td>\n",
       "      <td>0.129212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>9.025887</td>\n",
       "      <td>-19.420178</td>\n",
       "      <td>1.321479</td>\n",
       "      <td>0.440794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>8.998970</td>\n",
       "      <td>-19.781502</td>\n",
       "      <td>1.055043</td>\n",
       "      <td>-0.058375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9.187786</td>\n",
       "      <td>-19.859335</td>\n",
       "      <td>1.056138</td>\n",
       "      <td>-0.172806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>9.180293</td>\n",
       "      <td>-19.959347</td>\n",
       "      <td>0.697511</td>\n",
       "      <td>0.108731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>9.524613</td>\n",
       "      <td>-20.020833</td>\n",
       "      <td>1.119343</td>\n",
       "      <td>0.066564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>9.633443</td>\n",
       "      <td>-19.599954</td>\n",
       "      <td>1.336723</td>\n",
       "      <td>0.255515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>9.522356</td>\n",
       "      <td>-19.679449</td>\n",
       "      <td>1.367033</td>\n",
       "      <td>0.612731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>9.960627</td>\n",
       "      <td>-19.022793</td>\n",
       "      <td>1.596895</td>\n",
       "      <td>0.735369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>9.735163</td>\n",
       "      <td>-18.812758</td>\n",
       "      <td>1.288135</td>\n",
       "      <td>0.999685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>9.662709</td>\n",
       "      <td>-18.784331</td>\n",
       "      <td>1.741386</td>\n",
       "      <td>1.132604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>39.904374</td>\n",
       "      <td>0.184893</td>\n",
       "      <td>1.485044</td>\n",
       "      <td>0.670088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>10.334505</td>\n",
       "      <td>0.230987</td>\n",
       "      <td>1.972141</td>\n",
       "      <td>0.412289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>10.133042</td>\n",
       "      <td>0.871281</td>\n",
       "      <td>1.913733</td>\n",
       "      <td>0.614529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>10.258895</td>\n",
       "      <td>0.486600</td>\n",
       "      <td>2.151388</td>\n",
       "      <td>0.213664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>9.792009</td>\n",
       "      <td>0.800073</td>\n",
       "      <td>1.742501</td>\n",
       "      <td>0.509913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>9.909111</td>\n",
       "      <td>0.612172</td>\n",
       "      <td>1.757602</td>\n",
       "      <td>0.185826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>10.124215</td>\n",
       "      <td>0.609309</td>\n",
       "      <td>1.718569</td>\n",
       "      <td>-0.300031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>10.584127</td>\n",
       "      <td>0.343486</td>\n",
       "      <td>1.602878</td>\n",
       "      <td>-0.286739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>10.242992</td>\n",
       "      <td>0.743488</td>\n",
       "      <td>1.707858</td>\n",
       "      <td>-0.258555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>10.371311</td>\n",
       "      <td>0.499875</td>\n",
       "      <td>1.298326</td>\n",
       "      <td>-0.278375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            x0         x1        x2         x3\n",
       "0     3.000000  -3.000000  2.000000   1.000000\n",
       "1     3.060530  -2.887943  2.419412   0.908175\n",
       "2     2.773117  -3.003151  2.359755   0.756587\n",
       "3     2.816198  -2.953174  2.774531   0.618809\n",
       "4     2.350169  -2.929736  2.893389   1.019772\n",
       "5     2.085558  -3.566645  3.299516   0.859867\n",
       "6     2.177671  -3.340701  3.712991   0.423561\n",
       "7     2.012698  -3.302543  3.611960   0.424597\n",
       "8     1.783180  -3.384022  3.363775   0.011063\n",
       "9     1.590633  -3.465373  3.730589  -0.406819\n",
       "10    1.893002  -3.486088  3.773050   0.055843\n",
       "11    1.488150  -3.474417  4.014207   0.013911\n",
       "12    1.351290  -3.912176  3.804653   0.454455\n",
       "13    1.592446  -3.730252  3.551222   0.693068\n",
       "14    1.252394  -3.458582  3.352049   0.833815\n",
       "15    1.615042  -3.666618  2.875478   0.793399\n",
       "16    1.904937  -3.509938  3.097578   0.371861\n",
       "17    1.410280  -3.774180  3.353890   0.585673\n",
       "18    1.373855  -4.040941  3.002045   0.387076\n",
       "19    1.254923  -3.688319  3.142167   0.661533\n",
       "20    1.275424  -3.755768  4.109229  19.434913\n",
       "21    1.145376  -3.758103  3.602231   7.048872\n",
       "22    1.121012  -4.098771  3.485711  15.251952\n",
       "23    1.484584  -4.130221  3.791785   4.344360\n",
       "24    1.389605  -3.927507  3.437481  15.570344\n",
       "25    1.330106  -3.643611  4.395647   9.284515\n",
       "26    0.970960  -3.883088  4.682349   4.914358\n",
       "27    1.291141  -4.174954  5.074739   1.293672\n",
       "28    1.688795  -3.788701  5.482640  16.600688\n",
       "29    2.117202  -3.886541  5.419276  14.932876\n",
       "..         ...        ...       ...        ...\n",
       "170   8.893828 -19.481609  0.611777  -1.053146\n",
       "171   8.664630 -18.842794  0.852345  -0.571571\n",
       "172   9.126901 -19.306399  0.883148  -0.075540\n",
       "173   9.070452 -19.085070  1.294560   0.027764\n",
       "174   8.710409 -19.157064  1.650591  -0.286059\n",
       "175   8.721622 -19.446004  1.915065  -0.252924\n",
       "176   8.927362 -19.549335  2.106074   0.236873\n",
       "177   8.746750 -19.398734  2.012135   0.095387\n",
       "178   8.517075 -19.919382  1.749971  -0.229970\n",
       "179   8.925363 -19.494523  1.598497   0.129212\n",
       "180   9.025887 -19.420178  1.321479   0.440794\n",
       "181   8.998970 -19.781502  1.055043  -0.058375\n",
       "182   9.187786 -19.859335  1.056138  -0.172806\n",
       "183   9.180293 -19.959347  0.697511   0.108731\n",
       "184   9.524613 -20.020833  1.119343   0.066564\n",
       "185   9.633443 -19.599954  1.336723   0.255515\n",
       "186   9.522356 -19.679449  1.367033   0.612731\n",
       "187   9.960627 -19.022793  1.596895   0.735369\n",
       "188   9.735163 -18.812758  1.288135   0.999685\n",
       "189   9.662709 -18.784331  1.741386   1.132604\n",
       "190  39.904374   0.184893  1.485044   0.670088\n",
       "191  10.334505   0.230987  1.972141   0.412289\n",
       "192  10.133042   0.871281  1.913733   0.614529\n",
       "193  10.258895   0.486600  2.151388   0.213664\n",
       "194   9.792009   0.800073  1.742501   0.509913\n",
       "195   9.909111   0.612172  1.757602   0.185826\n",
       "196  10.124215   0.609309  1.718569  -0.300031\n",
       "197  10.584127   0.343486  1.602878  -0.286739\n",
       "198  10.242992   0.743488  1.707858  -0.258555\n",
       "199  10.371311   0.499875  1.298326  -0.278375\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dg.add_outliers({'extreme': [{'n': 0, 'timestamps': [(50,), (190,)],\n",
    "                                   'value': 30\n",
    "                                  }],\n",
    "                      'shift':   [{'n': 1, 'timestamps': [(100, 190)],\n",
    "                                   'shift_value': -20  # or 'value' (for a plain addition)\n",
    "                                  }],\n",
    "                      'trend':   [{'n': 2, 'timestamps': [(20, 150)],\n",
    "                                   'trend_value': 0.15\n",
    "                                  }],\n",
    "                      'variance':[{'n': 3, 'timestamps': [(20, 80)],\n",
    "                                   'variance_factor': 19\n",
    "                                  }]})\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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